The preferred embodiment of the invention presented above by way of example has the following characteristics:                a) the coding of the data for the purpose of transmission is effected by a turbocoder consisting of two convolutional coders and one interleaver (two-parity system), and        b) decoding after reception is effected by a turbodecoder consisting of two decoders (for example of the “BJCR” type or of the “SOVA” type, two interleavers, one deinterleaver, one adder and one decision unit.        
It will be recalled that a conventional turbocoder consists of two recursive systematic convolutional (RSC) coders and one interleaver, arranged as shown in FIG. 1. The turbocoder supplies as an output three series of binary elements (x, y1, y2), where x is the so-called systematic output of the turbocoder, that is to say one which has undergone no processing compared with the input signal, y1 is the output coded by the first RSC coder, and y2 is the output coded by the second RSC coder after passing through the interleaver π1.
FIG. 2 depicts an example of a conventional turbodecoder able to decode data supplied by a turbocoder like the one in FIG. 1. The inputs {circumflex over (x)}, ŷ1, ŷ2 of the turbodecoder are the outputs of the turbocoder as received by the decoder after passing through the transmission channel. Such a turbodecoder requires in particular two decoders, designated “decoder 1” and “decoder 2” in FIG. 2, for example of the BCJR type, that is to say using the algorithm of Bahl, Cocke, Jelinek and Raviv, or of the SOVA type (in English: “Soft Output Viterbi Algorithm”).
A conventional turbodecoder also requires looping back of the output of the deinterleaver π2 onto the input of the first decoder in order to transmit the so-called “extrinsic” information from the second decoder to the first decoder, and an adder 70 and a decision unit 80.
For more details on turbocodes, reference can usefully be made to the article by C. BERROU, A. GLAVIEUX and P. THITIMAJSHIMA entitled “Near Shannon Limit Error-Correcting Coding and Decoding: Turbo-Codes”, ICC '93, Geneva (published by IEEE, Piscataway, N.J., USA, 1993), or to the article by R. DE GAUDENZI and M. LUISE entitled “Audio and Video Digital Radio Broadcasting Systems and Techniques”, pages 215 to 226 of the Proceedings of the Tirrenia Sixth International Seminar on Digital Telecommunications (1993).
In the preferred embodiments of the invention described below, the data result from the processing of images by the so-called “decomposition into sub-bands” method.
It should be stated that the “decomposition into sub-bands” method or source coding “by decomposition into sub-bands” consists of dividing each image to be transmitted into several hierarchical blocks of data (referred to as “sub-bands”), and this iteratively. For example, at the first iteration, four sub-bands are created: the first contains the low frequencies of the image, the second the horizontal high frequencies, the third the vertical high frequencies and the fourth the diagonal high frequencies. Each sub-band contains a quarter of the data (pixels) of the original image. At the second iteration, the low frequency sub-band is itself decomposed into four new blocks containing the low frequencies, the horizontal high frequencies, the vertical high frequencies and the diagonal frequencies relating to this sub-band. The decomposition process is thus continued a certain number of times according to requirements.
This method is illustrated here by way of example by the decomposition into ten sub-bands (corresponding to three resolution levels) of the image depicted in FIG. 3a. The result is illustrated in FIG. 3b. The level of lowest resolution (top left-hand corner in FIG. 3b) contains the sub-bands LL3, HL3, LH3 and HH3; the second resolution contains the sub-bands HL2, LH2 and HH2; the highest resolution level contains the sub-bands HL1 (vertical high frequencies), LH1 (horizontal high frequencies) and HH1 (diagonal high frequencies). It should be noted that the sub-band LL3 is merely a reduction of the original image, whilst the other sub-bands identify details of this image.
The advantage of this coding by decomposition into sub-bands results from the fact that certain blocks are more important than others with regard to the quality of the image obtained after recomposition. This is because the low frequencies contribute more to the intelligibility of the image than the high frequencies.
The method of decomposition into sub-bands also offers the possibility of allocating to each sub-band a hierarchical rank DS in relation to the importance of these data (“Data Significance”). Thus, in order to exploit this possibility, in a known manner, in the example in question, a value which is all the higher, the lower the hierarchical importance of the corresponding block of data, will be given to DS; more precisely, there will be allocated successively to the sub-band LL3 a value of DS equal to 1, then to the sub-bands LH3, HL3 and HH3 a value of DS equal to 2, then to the sub-bands LH2, HL2 and HH2 a value DS equal to 3, and finally to the sub-bands LH1, HL1 and HH1 a value of DS equal to 4.
This example of the coding by decomposition into sub-bands (used conventionally for apportioning the compression level according to the importance of each block) illustrates the fact, essential for the invention, that there are in practice situations where the data to be transmitted lend themselves naturally to a classification in terms of their importance, which makes it possible, by virtue of the invention, namely by apportioning the quality of the channel decoding to this hierarchy, to make savings with regard to decoding and, thereby, with regard to the entire data coding/transmission/decoding process.